Trigger data quality monitor

• US 8,805,730 B2
• Filed: 06/01/2012
• Issued: 08/12/2014
• Est. Priority Date: 06/01/2012
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Yeri, Naveen G., Jena, Bibhudatta, Vedabrata, Arya Kumar, Katuri, Chandrashekar, Bendel, Timothy J., Joa, David, Mishra, Vikas, Allison, Thayer S. Jr., Verma, Deepak, Shao, Yanghong, Bajaj, Parul
• Primary Examiner(s): OYEBISI, OJO O
• Application Number:
  US13/486,593
• Publication Number:
  US 20130325674A1
• Time in Patent Office: 802 Days
• Field of Search: 705/35, 705/38, 705/39, 705/40, 705/30
• US Class Current: 705/30
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

Systems and methods for monitoring trigger data quality are provided herein. The systems and methods detect and report whether the current trigger counts are normal or flawed in real time. The systems and methods monitor the triggers to determine the accuracy, completeness, domain of values, and format of the trigger data. In the system and methods, account data associated with the one or more accounts is received and stored in a storage device; the account data is segregated into one or more periods of time; triggers associated with the one or more periods of time are identified based on transactions that occur during the one or more periods of time; a total transaction count for each of the triggers is calculated; and control limits are determined based on the transaction count for each of the triggers.

Claims

1. A system for monitoring trigger data quality associated with one or more accounts of a financial institution, the system comprising:

a computer apparatus including a processor and a memory; and

a trigger software module stored in the memory, comprising executable instructions that when executed by the processor cause the processor to;

receive account data associated with the one or more accounts;

store the account data in a storage device;

segregate the account data into one or more periods of time;

identify inbound transactions that regularly occur during the one or more periods of time, each period of time comprising a minimum and maximum number of days between the occurrence of the inbound transactions;

increase the length of each of the one or more periods of time by an additional number of days that is proportional to the length of each of the one or more periods of time;

identify triggers associated with the one or more periods of time based on the inbound transactions that occur during the one or more periods of time;

calculate a total transaction count for each of the triggers;

determine control limits based on the transaction count for each of the triggers;

calculate a lower control limit as the difference of the trimmed mean of the transaction count and the standard deviation of the transaction count;

calculate an upper control limit as the sum of the trimmed mean of the transaction count and the standard deviation of the transaction count; and

detect outliers based on the lower control limit and the upper control limit.

2. The system of claim 1, wherein the module is further configured to:

delete the outliers from the trigger data.

3. The system of claim 1, wherein the module is further configured to:

calculate a first lower control limit for transactions that occur during a first period of time and a second lower control limit for transactions that occur during a second period of time;

compare the first lower control limit and the second lower control limit;

determine the first lower control limit is higher or lower than the second lower control limit;

calculate a first upper control limit for transactions that occur during the first period of time and a second upper control limit for transaction that occur during the second period of time;

compare the first upper control limit and the second upper control limit; and

determine that the first upper control limit is higher or lower than the second upper control limit;

wherein the detection of the outliers is based on the comparison of the first lower control limit and the second lower control limit and the comparison of the first upper control limit and the second upper control limit.

4. The system of claim 3, wherein the module is further configured to:

determine the cause of the outliers.

5. The system of claim 3, wherein the module is further configured to:

determine that the outliers comprise the transactions that occur during the first period of time.

6. The system of claim 5, wherein the module is further configured to:

delete the transactions that occur during the first period of time from the trigger data.

7. The system of claim 5, wherein the module is further configured to:

delete at least one of the transactions that occur during the first period of time from the trigger data; and

recalculate the transaction count for a trigger associated with the first period of time.

8. The system of claim 5, wherein the module is further configured to:

determine that the transactions that occur during the second period of time comprise first parameters;

compare the transactions that occur during the first period of time with the transactions that occur during the second period of time;

determine that at least one of the transactions that occur during the first period of time comprises second parameters that fall outside of the range of the first parameters;

modify the first parameters.

9. The system of claim 3, wherein the module is configured to:

tag the outliers;

report the tagged outliers to an analyst.

10. The system of claim 1, wherein the module is configured to:

calculate a first transaction count for a first trigger associated with a first period of time

determine that the first transaction count for the first trigger is within an acceptable range of values;

calculate a second transaction count for a second trigger associated with a second period of time; and

determine that the second transaction count for the second trigger is outside of the acceptable range of values.

11. The system of claim 10, wherein the module is configured to:

tag the second trigger as an outlier trigger.

12. The system of claim 10, wherein the module is configured to:

tag the first trigger as a normal trigger.

13. A computer-implemented method for monitoring trigger data quality associated with one or more accounts of a financial institution, the method comprising:

receiving account data associated with the one or more accounts;

storing the account data in a storage device;

segregating, by a processor, the account data into one or more periods of time;

identifying, by a processor, inbound transactions that regularly occur during the one or more periods of time, each period of time comprising a minimum and maximum number of days between the occurrence of the inbound transactions;

increasing, by a processor, the length of each of the one or more periods of time by an additional number of days that is proportional to the length of each of the one or more periods of time;

identifying, by a processor, triggers associated with the one or more period of time based on the inbound transactions that occur during the one or more periods of time;

calculating, by a processor, total transaction count for each of the triggers;

determining, by a processor, control limits based on the transaction count for each of the triggers;

calculating, by a processor, a lower control limit as the difference of the trimmed mean of the transaction count and the standard deviation of the transaction count;

calculating, by a processor, an upper control limit as the sum of the trimmed mean of the transaction count and the standard deviation of the transaction count;

detecting, by a processor, outliers based on the lower control limit and upper control limit.

14. The method of claim 13, further comprising:

deleting, by a processor, the outliers from the trigger data.

15. The method of claim 13, further comprising:

determining, by a processor, the cause of the outliers.

16. The method of claim 13, further comprising:

determining, by a processor, that the transactions of the triggers comprise a threshold amount;

calculating, by a processor, a specific range of values for the transactions based on the threshold amount;

determining, by a processor, that the value of at least one of the transactions falls outside of the specific range of values; and

increasing or decreasing, by a processor, the threshold amount.

17. A computer program product for monitoring trigger data quality associated with one or more accounts of a financial institution, the computer program product comprising:

a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising;

computer readable program code configured to receive account data associated with the one or more accounts;

computer readable program code configured segregate the account data into one or more periods of time;

computer readable program code configured to identify inbound transactions that regularly occur during the one or more periods of time, each period of time comprising a minimum and maximum number of days between the occurrence of the inbound transactions;

computer readable program code configured to increase the length of each of the one or more periods of time by an additional number of days that is proportional to the length of each of the one or more periods of time;

computer readable program code configured to identify triggers associated with the one or more periods of time based on the inbound transactions that occur during the one or more periods of times;

computer readable program code configured to calculate a total transaction count for each of the triggers;

computer readable program code configured to determine control limits based on the transaction count for each of the triggers;

computer readable program code configured to calculate a lower control limit as the difference of the trimmed mean of the transaction count and the standard deviation of the transaction count;

computer readable program code configured to calculate an upper control limit as the sum of the trimmed mean of the transaction count and the standard deviation of the transaction count; and

computer readable program code configured to detect outliers based on the lower control limit and the upper control limit.

18. The computer program product of claim 17, further comprising computer readable program code configured to tag the outliers;

and report the outliers to an analyst.